Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Abstract

This paper studies the tracking control problem for an uncertain n -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

“…Based on Lyapunov theorem, a barrier Lyapunov function (BLF) method has been presented to solve output constraints for strict-feedback nonlinear systems [36], output feedback nonlinear systems [37], flexible systems [38], and robotic manipulator [35,39]. The BLF-based methods were also extended to solve state constraints [40][41][42]. Although the aforementioned results on the output or state constraints can guarantee that system outputs or states converge to a predefined bounded set, the predefined performance requirements on the convergence rate, maximum overshoot, and steady-state error have not been studied fully.…”

“…To solve partial tracking error constraints, a fuzzy dynamic surface control design was developed in [49,50] for a class of strict-feedback nonlinear systems by transforming the state tracking errors into new virtual variables. However, the existing control schemes, such as [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], can only guarantee the stability of closed-loop systems with different constraints, which are not capable of achieving the learning of unknown system dynamics. The main reason is that the derived closed-loop error system is extremely complex, such that its exponential convergence is difficult to be verified using the existing stability analysis tools.…”

Adaptive controller design for underwater snake robot with unmatched uncertainties

“…Currently, the adaptive control has already been researched in robot area [10][11][12][13][14][15][16]. Adaptive control based on the reference model of underwater snake-like robot has not been investigated in the literature yet.…”

Adaptive RBFNN control of robot manipulators with finite-time convergence

“…A robust adaptive position/force control scheme was proposed to deal with the holonomic constraints of the mobile robots in [6]. Recently, BLFs have been developed in nonlinear control design to deal with the state and output constrains [7], [8], [9], [10], [11], [12]. By adding constraints to the behavior of the state variables or system's outputs, tracking errors are indirectly constrained with the BLF constraint control method.…”

“…In [10], by applying a error transformation, a convenient BLF was constructed in a robust position controller to achieve prescribed performance constraints for a strict feedback nonlinear multiple-input-multiple-output (MIMO) dynamic system. A BLF is employed to deal with the tracking control with full-state constraints for a n-link robot with uncertain dynamics [11]. While in [12], an asymmetric time-varying BLF was presented to ensure the control of strict feedback nonlinear systems to satisfy prescribed constraints.…”